Using Field and Satellite Measurements to Improve Snow and Riming Processes in Cloud Resolving Models

The representation of clouds in climate and weather models is a driver in forecast uncertainty. Cloud microphysics parameterizations are challenged by having to represent a diverse range of ice species. Key characteristics of predicted ice species include habit and fall speed, and complex interactions that result from mixed-phased processes like riming. Our proposed activity leverages Global Precipitation Measurement (GPM) Mission ground validation studies to improve parameterization

The moral muteness of managers: an Anglo-American phenomenon? German and British managers and their moral reasoning about environmental sustainability in business

Several studies in the Anglo-American context have indicated that managers present themselves as morally neutral employees who act only in the best interest of the company by employing objective skills. The reluctance of managers to use moral arguments in business is further accentuated in the now common argument presented as a neutral fact that the company must always prioritise shareholder value. These and other commercial aims are seen as an objective reality in business, whilst questions about sustainability, environmental problems or fair trade are seen as emotional or moral ones; a phenomenon described as ‘moral muteness’. This research explores whether this ‘moral muteness’ is an Anglo-American phenomenon and/or whether managers in other countries - in this case Germany - might express themselves in a different way. The focus is on moral arguments around environmental sustainability and the implications of this study for cross-cultural management. This article is based on a qualitative, comparative cross-cultural study of British and German managers in the Food Retail and Energy Sectors. In line with the studies mentioned above, British managers placed a strong emphasis on their moral neutrality. In contrast, German managers tended to use moral arguments when discussing corporate greening, often giving such arguments more weight than financial arguments. Overall, the study suggests that the ‘moral muteness’ of managers is a British phenomenon and quite distinct from the German approach. The article ends in a short exploration of how this understanding can help managers better manage people, organisations and change across cultures

Machine Learning Classification of Flood Waters from Hurricanes Harvey and Florence as Captured by Synthetic Aperture Radar and Optical Remote Sensing

No abstract availabl

Advantages and Applications of Synthetic Aperture Radar as a Decision Support Tool

No abstract availabl

Using Satellite Imagery to Identify Tornado Damage Tracks and Recovery from the April 27, 2011 Severe Weather Outbreak

Emergency response to natural disasters requires coordination between multiple local, state, and federal agencies. Single, relatively weak tornado events may require comparatively simple response efforts; but larger "outbreak" events with multiple strong, long-track tornadoes can benefit from additional tools to help expedite these efforts. Meteorologists from NOAA's National Weather Service conduct field surveys to map tornado tracks, assess damage, and determine the tornado intensity following each event. Moderate and high resolution satellite imagery can support these surveys by providing a high-level view of the affected areas. Satellite imagery could then be used to target areas for immediate survey or to corroborate the results of the survey after it is completed. In this study, the feasibility of using satellite imagery to identify tornado damage tracks was determined by comparing the characteristics of tracks observed from low-earth orbit to tracks assessed during the official NWS storm survey process. Of the 68 NWS confirmed centerlines, 24 tracks (35.3%) could be distinguished from other surface features using satellite imagery. Within each EF category, 0% of EF-0, 3% of EF-1, 50% of EF-2, 77.7% of EF-3, 87.5% of EF-4 and 100% of EF-5 tornadoes were detected. It was shown that satellite data can be used to identify tornado damage tracks in MODIS and ASTER NDVI imagery, where damage to vegetation creates a sharp drop in values though the minimum EF-category which can be detected is dependent upon the type of sensor used and underlying vegetation. Near-real time data from moderate resolution sensors compare favorably to field surveys after the event and suggest that the data can provide some value in the assessment process

Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratorys Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018

Evaluation of Mixed-Phase Microphysics Within Winter Storms using Field Data and In Situ Observations

It is hypothesized that microphysical predictions have greater uncertainties/errors when there are complex interactions that result from mixedphased processes like riming. Use Global Precipitation Measurement (GPM) Mission ground validation studies in Ontario, Canada to verify and improve parameterization

Comparisons of Modeled and Observed Reflectivities and Fall Speeds for Snowfall of Varied Riming Degree During Winter Storms on Long Island, New York

Derived radar reflectivity and fall speed for four Weather Research and Forecasting model bulk microphysical parameterizations (BMPs) run at 1.33 km grid spacing are compared with ground-based, vertically-pointing Ku-band radar, scanning S- band radar, and in situ measurements at Stony Brook, NY. Simulations were partitioned into periods of observed riming degree as determined manually using a stereo microscope and camera during nine winter storms. Simulations were examined to determine whether the selected BMPs captured the effects of varying riming intensities, provided a reasonable match to the vertical structure of radar reflectivity or fall speed, and whether they produced reasonable surface fall speed distributions. Schemes assuming non spherical mass-diameter relationships yielded reflectivity distributions closer to observed values. All four schemes examined in this study provided a better match to the observed, vertical structure of reflectivity during moderate riming than light riming periods. The comparison of observed and simulated snow fall speeds had mixed results. One BMP produced episodes of excessive cloud water at times, resulting in fall speeds that were too large. However, most schemes had frequent periods of little or no cloud water during moderate riming periods and thus underpredicted the snow fall speeds at lower levels. Short, 1-4 hour periods with relatively steady snow conditions were used to compare BMP and observed size and fall speed distributions. These limited data suggest the examined BMPs underpredict fall speeds of cold-type snow habits and underrepresent aggregates larger than 4 mm diameter

Development of RGB Composite Imagery for Operational Weather Forecasting Applications

No abstract availabl

Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

Severe thunderstorms can produce large hail that causes property damage, livestock fatalities, and crop failure. However, detailed storm surveys of hail damage conducted by the National Weather Service (NWS) are not required. Current gaps also exist between Storm Prediction Center (SPC) hail damage estimates and cropinsurance payouts. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites can be used to support NWS damage assessments, particularly to crops during the growing season. The twoday severe weather event across western Nebraska and central Kansas during 910 August 2011 offers a case study for investigating hail damage signatures by examining changes in Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery. By analyzing hail damage swaths in satellite imagery, potential economic losses due to crop damage can be quantified and further improve the estimation of weather impacts on agriculture without significantly increasing manpower requirements